Tag Archives: Technology

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Quantifying the ROI of Environmental Monitoring Program Automation

By Joseph Heinzelmann
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Dollar

The COVID-19 pandemic heightened the urgency for food brands to adopt technology solutions that support remote management of environmental monitoring programs (EMPs) as they strive to provide safe products to customers. While digital transformation has progressed within the food safety industry, food and beverage manufacturers often have lower profitability as compared to other manufacturing industries, such as pharmaceutical and high-tech equipment, which can lead to smaller IT spend.1 Many companies still rely on manual processes for environmental monitoring and reporting, which are prone to error, fail to provide organizations with visibility into all of their facilities and limit the ability to quickly take corrective actions.

Despite growing recognition of the value of automating testing, diagnostics, corrective actions and analytic workflows to prevent contamination issues in food production environments, barriers to adoption persist. One key obstacle is the recurring mindset that food safety is a necessary compliance cost. Instead, we need to recognize that EMP workflow automation can create real business value. While the downside of food safety issues is easy to quantify, organizations still struggle to understand the upside, such as positive contributions to productivity and a stronger bottom-line achieved by automating certain food safety processes.

To understand how organizations are using workflow automation and analytics to drive quantifiable business ROI, a two-year study that included interviews and anonymized data collection with food safety, operations, and executive leadership at 34 food organizations was conducted.

The respondents represent more than 120 facilities using advanced EMP workflow automation and analytics. Based on the interviews and the shared experience of food organization leaders, two key examples emerged that demonstrate the ROI of EMP automation.

Improved Production Performance

According to those interviewed, one of the primary benefits of EMP automation (and driver of ROI) is minimizing production disruptions. A temporary conveyor shutdown, unplanned cleaning, or extensive investigatory testing can add up to an astounding 500 hours annually at a multi-facility organization, and cost on average $20,000 to $30,000 per hour.2 So, it’s obvious that eliminating costly disruptions and downtime has a direct impact on ROI from this perspective.

But organizations with systems where information collected through the EMP is highly accessible have another advantage. They are able to take corrective actions to reduce production impacts very quickly. In some cases, even before a disruption happens.
By automatically feeding EMP data into an analytics program, organizations can rapidly detect the root cause of issues and implement corrective actions BEFORE issues cause production delays or shutdowns.

In one example, over the course of several months, a large dairy company with manual EMP processes automated its food safety workflows, improved efficiencies, reduced pathogen positives and improved its bottom line. At the start of the study, the company increased systematic pathogen testing schedules to identify where issues existed and understand the effectiveness of current sanitation efforts. With improved access to data on testing, test types and correlated sanitation procedures, the company was able to implement a revamped remediation program with more effective corrective action steps.

Ultimately, the automated workflows and analytics led to reduced positive results and more efficient EMP operations for the company as compared to the “crisis-mode” approach of the past. The associated costs of waste, rework, delayed production starts, and downtime caused by food safety issues were significantly reduced as illustrated in Figure 1.

EMP automation
Figure 1: Reduction of food safety testing costs through EMP automation. Customer Study 2016-2018. All figures courtesy of Corvium, Inc.

Quantifying the ROI of Production Performance Improvements

The financial impact of reducing production downtime by just 90 minutes per week can be dramatic when looked at by cumulative results over multiple weeks. In fact, eliminating just a few delayed starts or unplanned re-cleaning can have significant financial gains.

Figure 2 shows the business impact of gaining 90 minutes of production up-time per week by automating food safety operations. For the purposes of this analysis, the “sample organization” depicted operates two facilities where there are assumptions that down-time equates to a cost value of $30,000 per hour, and that both plants experience an average of 90 minutes of downtime per week that can be re-gained.

Production Performance Improvement ROI Calculation
Figure 2: Sample Production Performance Improvement ROI Calculation.

Reduced Food Waste

The second key insight uncovered in the two-year study was the impact that automating the EMP process had on waste. An estimated 30–40% of all food produced in the United States is wasted, and preventable food safety and quality issues account for a substantial portion of this waste.3

A key challenge shared by study participants was detecting food safety issues early enough to avoid wasting an entire production run. Clearly, the later in a processing or manufacturing run that issues are discovered, the greater the potential waste. To limit this, organizations needed near real-time visibility into relevant food safety and EMP data.

By automating EMP workflows, they solved this issue and created value. By tracking and analyzing data in near real time, production teams were able to keep up with ever-moving production schedules. They could define rules to trigger the system to automatically analyze diagnostic results data and alert stakeholders to outliers. Impacted food product could be quickly identified and quarantined when needed before an entire production run was wasted.

Companies included in the study realized substantial benefits from the increased efficiencies in their testing program. According to a food safety quality assurance manager at a large U.S. protein manufacturer, “Our environmental monitoring program has reached new heights in terms of accuracy, communication, visibility and efficiency. Manual, time-intensive tasks have been automated and optimized, such as the ability to search individual sample or submittal IDs, locate them quickly and make any necessary changes.”

Quantifying the ROI of Food Waste Reductions

Figure 3 shows how measuring the business impact of gaining back just 10% of scrapped food per week. For the purposes of this analysis, the “sample organization” depicted operates two facilities where there are 500 lbs. of finished product scrapped each week, and the value per pound of finished product is valued at a cost of $1 per pound.

Sample Waste Reduction ROI Calculation
Figure 3. Sample Waste Reduction ROI Calculation.

Conclusion

Automating EMP workflows decreases the time required to receive and analyze critical EMP data, helping food manufacturers achieve significant improvements in production performance, waste reduction and overall testing efficiency. By using these same ROI calculations, food brands can better illustrate how improved food safety processes can build value, and help leaders see food safety as a brand imperative rather than a cost center. As food organizations progress through each stage of digital transformation, studies like this can show real-world examples of business challenges and how other organizations uncovered value in adoption of new technologies and tools.

References

  1. CSIMarket, Inc. (2021). Total Market Profitability.
  2. Senkbeil, T. (2014). Built to Last: Maintaining Reliability and Uptime of Critical Connected Systems in Industrial Settings. Anixter.
  3. USDA. Food Waste FAQs.
GFSI, The Consumer Goods Forum

Reimagining Food Safety Through Transparency and Open Dialogue

By Maria Fontanazza
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GFSI, The Consumer Goods Forum

Last year’s annual GFSI Conference was held in Seattle just weeks before the World Health Organization (WHO) declared COVID-19 a pandemic. This year’s event looked very different, as it joined the virtual event circuit—with hundreds of attendees gathering from across the globe, but from the comfort of their homes and offices. The 2021 GFSI Conference reflected on lessons learned over the past year, the fundamentals of building a better food system, and the idea that food safety is a collaborative effort that also encompasses training programs, effectively leveraging data and capacity building.

The pandemic provided the opportunity to reimagine safer, more resilient and sustainable food systems, said Dr. Naoki Yamamoto, universal health coverage, assistant director-general, UHC, Healthier populations at WHO. She also offered three clear messages that came out of the pandemic:

  • Food safety is a public health priority and a basic human right. Safe food is not a luxury.
  • Food safety is a shared responsibility. Everyone in the food chain must understand this responsibility and work towards a common goal.
  • Good public private partnership can bring new opportunities and innovative solutions for food safety. We need to seek more collaborative approaches when working across sectors to achieve foods safety.

During the session “Ready for Anything: How Resiliency and Technology Will Build Consumer Trust and Help Us Mitigate Disruption in the 21st Century”, industry leaders discussed how the pandemic reminded us that a crisis can come in many forms, and how applying the right strategy and technology can help us remain resilient and equipped to address the challenges, said Erica Sheward, GFSI director.

“When you think about business resiliency—it’s about our own, but most importantly, it’s about helping our customers become more resilient to those disruptions,” said Christophe Beck, president and CEO of Ecolab. He added that being able to predict disruptions, help customers respond to those disruptions, and provide real-time control to learn and prepare for the next pandemic or serious crisis is critical. Companies need to ensure their technology systems and contingency plans are ready to go, advised David Maclennan, chairman and CEO of Cargill. The key to a resilient food supply chain system is access and the ability to keep food moving across borders. And above all, whether dealing with a health crisis or a food safety crisis, consumers must always be front and center, said Natasa Matyasova, head of quality management at Nestle. “In short term, [it’s] first people, then business contingency, and then help the community as needed,” she said.

Olga Pawluczyk, P&P Optica
FST Soapbox

Assessing Detection Systems to Make Food Safer

By Olga Pawluczyk
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Olga Pawluczyk, P&P Optica

It is an exciting time to be in the food industry. Consumers are ever more aware of what they are eating and more demanding of quality. And the vital need to reduce global food waste is transforming how we produce and consume food. This is driving innovation all the way along the supply chain, from gate to plate.

One of the biggest areas of opportunity for the industry to increase automation and improve food safety is in the processing plant. The challenges processors have faced in the last 12 months have accelerated the focus on optimizing resources and the drive for more adoption of new technology.

Foreign material contamination is a growing issue in the meat industry and new types of detection systems are emerging to help address this challenge. As Casey Gallimore, director of regulatory and scientific affairs at the North American Meat Institute, highlighted in a recent webinar, 2019 was a record year for the number of recalls related to foreign object contamination, which totaled 27% of all FSIS recalls in that year.

“There are a number of potential reasons why recalls due to foreign object contamination have increased over the years: Greater regulatory focus, more discerning consumers, [and] more automation in plants. But one important reason for this trend is that we have a lot of new technology to help detect more, [but] we are not necessarily using it to its full potential,” said Gallimore. “As an industry, we have a strong track record of working together to provide industry-wide solutions to industry-wide problems. And I believe that education is key to understanding how different detection systems—often used together—can increase the safety and quality of our food.”

Types of Detection Systems

Processors use many different detection systems to find foreign materials in their products. Equipment such as x-rays and metal detectors, which have been used for many years, are not effective against many of today’s contaminants: Plastics, rubber, cardboard and glass. And even the most well trained inspectors are affected by fatigue, distraction, discomfort and many other factors. A multi-hurdle approach is imperative, and new technologies like vision systems need to be considered.

Vision systems, such as cameras, multi-spectral, and hyperspectral imaging systems can find objects, such as low-density plastics, that may have been missed by other detection methods. Yet, depending on the system, their performance and capabilities can vary widely.

Camera-based systems are the most similar to the human eye. These systems are good for distinguishing objects of varying size and shape, albeit in two-dimensions rather than three. But they become less effective in situations with low contrast between the background and the object being detected. Clear plastics are a good example of this.

Multi-spectral systems are able to see more colors, including wavelengths outside of the visible spectrum. However, multispectral systems are set up to use only specific wavelengths, which are selected based on the materials that the system is expected to detect. That means that multispectral systems can identify some chemical as well as visual properties of materials, based on those specific wavelengths. It also means that other materials, which the system has not been designed to find, will likely not be detected by a multispectral system.

Another relatively new type of vision system uses hyperspectral imaging. These systems use chemistry to detect differences in the materials being inspected and therefore recognize a broad range of different contaminants. They are especially good at seeing objects that cameras or human inspectors may miss and at identifying the specific contaminant that’s been detected. The same system can assess quality metrics such as composition and identify product flaws such as woody breast in chicken. Hyperspectral systems also gather tremendous amounts of chemistry data about the products they are monitoring and can use artificial intelligence and machine learning to get a more holistic picture of what is happening in the plant over time, and how to prevent future contamination issues. This might include identifying issues with a specific supplier, training or other process challenges on one line (or in one shift), or machinery in the plant that is causing ongoing contamination problems.

Many processors are considering implementing new inspection systems, and are struggling to understand how to compare the expected performance of different systems. One relatively simple methodology that can be used to evaluate system performance is, despite its simplicity, called a “Confusion Matrix”.

The Confusion Matrix

A confusion matrix is often used in machine learning. It compares the expected outcome of an event with the actual outcome in order to understand the reliability of a test.

Figure 1 shows four possible outcomes for any kind of test.

Actual (True Condition)

Predicted

(Measured Outcome)

Positive (P) Negative (N)
Positive Detection True Positives (TP) False Positives (FP)
Negative Detection False Negatives (FN) True Negatives (TN)
P = TP + FN N = FP + TN
Figure 1. Confusion Matrix

But what does a confusion matrix tell us, and how can it help us assess a detection system?

The matrix shows us that a detection system may incorrectly register a positive or negative detection event—known as a ‘False Positive’ or ‘False Negative’.

As an example, say we are testing for a disease such as COVID-19. We want to know how often our system will give us a True Positive (detecting COVID when it *IS* present) versus a False Positive (detecting COVID when it *IS NOT* present).

Let’s apply this to processing. If you are using an x-ray to detect foreign objects, a small piece of plastic or wood would pass through unnoticed. This is a False Negative. By contrast, a system that uses hyperspectral imaging would easily identify that same piece of plastic or wood, because it has a different chemical signature from the product you’re processing. This is a True Positive.

A high rate of false negatives—failing to identify existing foreign materials—can mean contaminated product ends up in the hands of consumers.

The other side of the coin is false positives, meaning that the detector believes foreign material to be present when in fact it is not. A high rate of False Positives can lead to significant and unnecessary product wastage, or in time lost investigating an incident that didn’t actually occur (see Figure 2).

True Positives and False Positives
Figure 2. Balance of True Positives and False Positives

The secret to a good detection system lies in carefully balancing the rates of true positives and false positives by adjusting the sensitivity of a system.

This is where testing comes in. By adjusting a system and testing under different conditions, and then plotting these outcomes on the confusion matrix, you get an accurate picture of the system’s performance.

Effectiveness of a Detector

Detection is not just the act of seeing. It is the act of making a decision based on what you have seen, by understanding whether something of importance has occurred. Many factors influence the effectiveness of any detection system.

Resolution. This is the smallest size of object that can possibly be detected. For example, when you look at a photograph, the resolution affects how closely you can zoom in on an image before it becomes blurry.

Signal to noise ratio. This measures the electronic “noise” of the detector and compares it with the “background noise” that may interfere with the signals received by the detector. Too much background noise makes it harder to identify a foreign object.

Speed of acquisition. This measures how fast the detector can process the signals it receives. Motion limits what you can see. As line speeds increase, this impacts what detectors are able to pick up.

Material being detected. The type of material being detected and its properties will have a significant impact on the likelihood of detection. As previously mentioned, for example, x-rays are unlikely to detect low-density materials such as cardboard, resulting in a high number of False Negatives.

Presentation or location of material being detected. Materials that are underneath another object, that are presented on an angle, are too similar to the product being inspected, or are partially obstructed may be more difficult for some detectors to find. This also presents a risk of False Negatives.

Complexity of the product under inspection. Product composition and appearance vary. For example, just like the human eye, finding a small object on a uniformly illuminated and uniform color background like a white kitchen floor is much easier than finding the same small object on a complex background like industrial carpet. Coarsely ground meat might be more difficult to detect than uniform back fat layers, for example.

Environment. Conditions such as temperature and humidity will have a significant effect on detection.

Detection Curves

To understand system performance even better, we can use a detection curve. This plots out the likelihood of detection against different variables (e.g., object size) and allows us to objectively compare how these different factors impact the performance of each system.

Figure 3 shows how this looks when plotted as a curve, with object size on the x-axis (horizontal) and the probability of detection (a True Positive from the Confusion Matrix) on the y-axis (vertical). It shows three examples of possible detection curves, depending on the detector being used.

Detection curves
Figure 3. Examples of detection curves for different detectors. Probability of detection of an object increases as the size of the object increases.

A detection curve tells you both the smallest and largest object that a detector will find and the probability that it will be found.

In the example presented by Figure 3, Detector 3 can see essentially 100% of large and very large objects, as can Detector 2. But Detector 3 is also more likely than the other two systems in the example to see microscopic objects. Based on this detection curve it would likely be the best option if the goal were to detect as many foreign objects as possible, of all sizes.

Of course, the performance of a detector is determined by multiple measures, not just size,

Detection capability can be improved for most detection systems, but typically comes at a significant cost: Increasing sensitivity will increase the number of false positives, resulting in increased product rejection. This is why looking at the detection curve together with the false-positive/false-negative rates for any detection system gives us a clear picture of its performance and is invaluable for food processing plants when selecting a system.

Using the confusion matrix and a detection curve, processors can compare different detection systems on an apples-to-apples basis. They can easily see whether a system can identify small, tiny or microscopic objects and, crucially, how often it will identify them.

Every detection method—X -ray, metal detection, vision systems, manual inspection—presents a trade-off between actual (correct) detection, rejection of good product (false positive) and missed detections (false negative). This simple way to compare differences means processors can make the right decision for the specific needs of their plant, based on easily gathered information. For all of us data geeks out there, that sounds like the Holy Grail.

Hussain Suleman, Sigfox
Retail Food Safety Forum

How to Use the IoT to Keep Your Restaurant Clean and Safe

By Hussain Suleman
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Hussain Suleman, Sigfox

The COVID-19 pandemic has brought challenges to all industries, and many restaurants have been forced to close their doors permanently. Restaurant owners have struggled due to COVID-19 restrictions that have drastically cut the number of customers they can serve—whether as a result of an indoor dining ban or capacity limits. Those that have been allowed to re-open are being stretched to meet new guidelines to keep guests safe and comfortable while dining. Not only do restaurant owners need to make sure their restaurants are COVID-safe, but they also need to ensure they are providing the quality service and meals their customers have come to know and love. The Internet of Things (IoT) can not only ease the burden of implementing new protocols while also ensuring a clean and safe environment for both employees and patrons, but also help restaurants enhance efficiency.

The following are some points on how the IoT can help restaurants not only survive, but thrive amid the pandemic.

Monitoring Cleaning

Easy-to-deploy IoT-enabled devices provide several benefits to QSRs, including the monitoring of employee hand washing stations, dishwashing water temperatures, sanitizer solution concentrations and customer bathroom usage frequency to ensure constant compliance with cleanliness standards.

By placing sensors on tables and work lines, restaurant owners can collect valuable data and insights in real time. For example, the sensors can share information about how often tables are being cleaned. This information will help owners trust that tables are being cleaned thoroughly in between each use.

Sensors can also be placed on washbasins to monitor employee hand washing. Sensors on the sinks will not only confirm that employees’ hands have been washed, but they will also share exactly how long employees washed their hands. That way, owners can have peace of mind knowing employees’ hands and restaurant surfaces are properly sanitized before customers sit down to eat. With door sensors monitoring customer bathrooms, store owners can ensure adequate cleaning is allocated based on frequency of usage.

Rodent Detection

Owners can also have peace of mind knowing their restaurant is rodent free by using IoT monitored sensors. Rodents are especially dangerous to be found lurking in restaurants because they carry diseases and can cause electrical fires. Devices can be placed throughout the restaurant to detect any motion that occurs. When the devices detect a motion, restaurant owners will receive notifications and will be immediately aware of any rodents that may have snuck into the restaurant.

These sensors give restaurant owners a chance to proactively address a rodent issue before it causes damage to their business.

Routine Monitoring

In addition to monitoring sanitation and detecting motion, restaurant owners can leverage the IoT many other ways. For example, IoT devices can be placed on trash bins to alert when they are full and ready to be taken out. They can also be placed near pipes to detect a leak. Sensors can also be placed on all refrigerators to detect temperature. With accurate updates on refrigerators’ temperatures, restaurant owners can easily monitor and ensure that food is stored at the appropriate temperature around the clock—and be immediately alerted if a power issue causes temperatures to change.

IoT devices can offer restaurant owners insights to help them change their operations and behavior for the better. While everyone is eager to go back to “normal” and want our favorite restaurants to re-open as soon as possible, it is important that restaurant owners have the tools needed to reopen safely—and create efficiencies that can help recoup lost income due to COVID-19 restrictions. Restaurant owners looking to receive real-time, accurate data and insights to help run their restaurants more efficiently and ensure a safe and comfortable experience for customers can turn to the IoT to achieve their goals.

GFSI, The Consumer Goods Forum

Reset, Rethink, Recharge: First Virtual GFSI Conference to Address Urgent Topics in Food Safety

By Food Safety Tech Staff
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GFSI, The Consumer Goods Forum

This year’s GFSI Conference will take place March 23–25 and bring together experts, decision makers and innovators in the food industry. With the theme of “rethink, reset, recharge”, the three-day virtual program includes online networking features to allow attendees to connect with professionals across the globe, and sessions that explore COVID-19; supply chain disruption and public health; building consumer trust and transparency; sharing best practices; and technologies shaping the future of food safety.

“Collaboration to ensure safe food for consumers everywhere and sustainable food systems has never been more critical – and this event provides a major opportunity to learn from an unprecedented period and move forwards in the best possible way. We’re excited by the chance to help colleagues across the industry build on the ingenuity, resilience and dedication shown by the food industry over the past 12 months,” said Erica Sheward, director of GFSI, in a press release. “With the conference taking place virtually for the first time, it’s easier than ever before for food industry professionals to get involved—and we’re urging people from all corners of the globe to ensure they’re part of this unique and collaborative forum. Food safety is everyone’s business, and we must continue to work together to build consumers’ trust in the food they buy.”

More information about the GFSI conference, along with registration, agenda and partner details, can be found on the event website.

GFSI is a partner organization for the 2021 Food Safety Consortium Virtual Conference Series.

Jason Chester, InfinityQS
FST Soapbox

Resilience for Tomorrow Begins with Digital Transformation Today

By Jason Chester
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Jason Chester, InfinityQS

COVID-19 has been a sharp wake-up call for many food manufacturers in the need for resilient production environments that can readily respond to large and sudden changes, including fluctuations in demand and disruptive external events. This means being able to optimize operations for the following:

  • Efficiency: Where you can achieve constant output even when given fewer inputs—such as in workforce availability or resources. This was especially important when the pandemic caused widespread supply shortages, as well as staffing shortages due to social distancing measures.
  • Productivity: When you can ensure that, given the amount of available input (i.e., raw ingredients, manpower, equipment availability), you can maintain a consistent output to meet demand in the marketplace.
  • Flexibility: Where you can rapidly and intelligently adapt your processes in the face of change, in ways that are in the best interest of your business, the supply chain, and the consumers who purchase and trust in your products.

That trust is paramount, as manufacturers must continue to uphold quality and safety standards—especially during a time when public health is of the upmost importance. But between operational challenges and managing product quality, that’s a lot for manufacturers to wade through during a crisis.

To navigate the current COVID reality and improve response to future events, more organizations are looking to harness the power of data to enable agile decision-making and, in turn, build more resilient production environments.

Harnessing the Power of Data

The key to harnessing data for agile decisions is to aggregate end-to-end process information and make it available in real time. When you can achieve that, it’s possible to run analytics and derive timely insights into every facet of production. Those insights can be used to increase efficiency, productivity and flexibility—as well as ensure product quality and safety—even amidst upheaval.

When looking at solutions to aggregate data from a single site—or better yet, multiple sites—all roads lead to the cloud. Namely, cloud-based quality intelligence solutions can decouple the data from physical locations—such as paper checklists, forms, or supervisory control and data acquisition (SCADA) and human-machine interfaces (HMI) systems—and centralize what’s collected digitally in a unified repository. The data can then be accessed, analyzed, and consumed by those who need actionable insights from anywhere, at any time, and on any device, making cloud an ideal solution for connecting on-site operators and remote employees.

Digital transformation
When process and quality data are centralized and standardized on the cloud, they can be leveraged for real-time monitoring and timely response to issues—from anywhere and at any time. (Image courtesy of InfinityQS)

An Opportunity for Broader Transformation

In migrating to the cloud, manufacturers open the opportunity to break away from the legacy, manual processes of yesterday and transition to more nimble, digitally enabled environments of tomorrow. For example, manual processes are often highly dependent on individual operator knowledge, experience and judgement. As the pandemic has shown, such institutional knowledge can be lost when employees become ill, or are unavailable due to self-isolation or travel restrictions, presenting a risk to operational efficiency and productivity. But if that valuable institutional knowledge were captured and codified in a quality intelligence solution as predefined workflows and prescriptive instructions, then a manufacturer could more easily move their resources and personnel around as necessary and find comfort knowing that processes will be executed according to best practices.

For many organizations, this would be a remarkable transformation in the ways of working, where data and digital technologies can augment human capacity and flexibility. Take for instance, in traditional production environments, a lot of human effort is spent on monitoring lines to catch process deviations or events like machine anomalies or quality issues. Using real-time data, next-generation solutions can take on that burden and continuously monitor what’s happening on the plant floor—only alerting relevant teams when an issue arises and they need to intervene. Manufacturers can thereby redeploy people to other tasks, while minimizing the amount of resources necessary to manage product quality and safety during daily production and in the event of disruption.

Ensuring Quality Upstream and Downstream

One company that has succeeded in digital transformation is King & Prince, a manufacturer of breaded, battered and seasoned seafood. When the company digitized its manufacturing processes, it centralized the quality data from all points of origin in a single database. The resulting real-time visibility enables King & Prince to monitor quality on more than 100 processes across three U.S. plants, as well as throughout a widespread network of global suppliers.

With this type of real-time visibility, a company can work with suppliers to correct any quality issues before raw materials are shipped to the United States, which directly translates to a better final product. This insight also helps plant-based procurement managers determine which suppliers to use. Within its own plants, operators receive alerts during production if there are any variations in the data that may indicate inconsistencies. They can thereby stop the process, make necessary adjustments, and use the data again to confirm when everything is back on track.

During finished product inspections, the company can also review the captured data to determine if they need to finetune any processes upstream and respond sooner to prevent issues from making it downstream to the consumer level. Overall, the company is able to better uphold its quality and safety standards, with the number of customer complaints regarding its seafood products dropping to less than one per million pounds sold year over year—and that’s all thanks to the harnessing of data in a digitally enabled production environment.

There’s No Time Like the Present

In truth, technologies like the cloud and quality intelligence solutions, and even the concept of digital transformation, aren’t new. They’ve been on many company agendas for some time, but just haven’t been a high priority. But when the pandemic hit, organizations were suddenly faced with the vulnerabilities of their long-held operational processes and legacy technologies. Now, with the urgency surrounding the need for resilient production environments, these same companies are thinking about how to tactically achieve digital transformation in the span of a few weeks or months rather than years.

Yet while digital transformation may sound like a tremendous initiative with high risks and expenses, it’s more tangible than some may think. For example, cloud-based Software-as-a-Service (SaaS) solutions offer flexible subscription-based models that keep costs low on top of rapid scalability. Digital transformation doesn’t have to be an all-or-nothing endeavor either. In fact, it can be better to progress incrementally, starting first with the manufacturing areas that are most in need or have the most issues. This minimizes unnecessary risk, makes digital transformation more achievable and realistic over short timeframes, and avoids overwhelming already maxed out operational and IT teams.

All things must pass. The pandemic will eventually be over. But in its wake will be a permanent legacy on not just society, but also on the manufacturing sector. In my opinion, digital transformation is a fundamental basis for building resilience into the modern food production environment. Now, more than ever, is the time to address that opportunity head on.

Frank Yiannas, FDA, food safety

Ten Years Later, a Reflection on FSMA’s Accomplishments

Frank Yiannas, FDA, food safety

It may be hard to believe that 10 years have passed since FSMA became law. The risk-based preventive approach to growing, manufacturing and processing, packing, storing and transporting food has transformed the industry and the nation’s food system. Today, on the anniversary of FSMA, FDA Deputy Commissioner for Food Policy and Response Frank Yiannas takes a look at where it all began and walks us through progress, accomplishments and what the future holds.

  • Seven foundational rules established, and the proposed Food Traceability Rule (September 2020) positioned to harmonized traceability
  • Global partnerships (Canada, Mexico, Europe, and China) to strengthen safety of imported food
  • Investment in cooperative agreement programs to support compliance with FSMA rules at the state level
  • Looking forward: New Era of Smarter Food Safety, with blueprint released in July 2020 creates a 10-year roadmap for a more “digital, traceable and safer food system”
    • Incentivizing industry to adopt new technologies that facilitate full traceability
    • Emphasis on food safety culture on farms and in food facilities
    • Improving root cause analysis when preventive control measures fail

“Have we accomplished everything we wanted to help ensure that the food you serve your family is safe? The honest answer is that we’re still working on that. We are working diligently to ensure that remaining FSMA rules and related guidance documents are finalized and implemented,” said Yiannas in the FDA Voices blog. “But even when we have reached all of those milestones, we will always be working with industry on continuous improvement based on the latest science and the application of new technologies. Every day we will do our utmost to make our nation’s food as safe as it can be.”

Emily Newton, Revolutionized Magazine
FST Soapbox

How Can Preventive Maintenance Save Food Processors Money?

By Emily Newton
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Emily Newton, Revolutionized Magazine

The right preventive maintenance approach can improve food safety while saving money. With the right plan, food processing professionals can prevent serious machine failure, decrease maintenance costs and get a better sense of which machines may be more trouble than they’re worth.

However, not every preventive maintenance plan is guaranteed to help processors cut costs. Investing in the right strategy and tools will be necessary for a business that wants to save money with effective maintenance.

How an Effective Preventive Maintenance Approach Can Save Money

To start, the food safety benefits of a preventive maintenance program can help food processors avoid significant troubles down the line. Contamination and recalls will cost time and money.

They can also damage the professional relationships that businesses have with buyers. Recalls are extraordinarily expensive for food and beverage companies, costing an average of $10 million per recall, according to one joint study from the Food Marketing Institute and the Consumer Brands Association (formerly the Grocery Manufacturers Association).

Preventive maintenance can also extend machines’ life spans, giving a company more time before they’ll need to completely replace or rebuild a piece of equipment. Over time, this will help a business prevent machine failure or injuries resulting from improper machine behavior or function. In some cases, it can also mean cheaper repairs and less downtime.

Improving Records With the Right Plan

An effective preventive maintenance plan also generates a significant and detailed archive of maintenance records.

If a plan is implemented correctly, technicians will create a record every time they inspect, repair or otherwise maintain a particular machine. These records will be an invaluable asset in the event of an in-house or third-party audit, as they can help prove that machines have been properly lubricated, calibrated and otherwise maintained.

If a food processing business needs to resell a particular piece of equipment, they’ll also have a full service record that can help them establish the machine’s value.

Over time, the records will also give a highly accurate sense of how expensive the machines really are across an entire business. If the staff records repairs performed, tools used and resources and time spent, professionals can quickly tabulate each machine’s cost concerning man-hours or resources needed. These logs can help single out machinery that may be more trouble than it’s worth and plan future buying decisions.

With a digital system, like a computerized maintenance management system (CMMS), managers can automate most of the administrative work that goes into a preventive maintenance plan.

Modern CMMS tech also provides a few additional benefits beyond streamlining recordkeeping. For example, if a business is up against a major maintenance backlog or trying to balance limited resources against necessary repairs and checkups, a CMMS can help optimize their use of resources. As a result, they can make the most of the time, money and tools they have.

Common Preventive Maintenance Pitfalls

Typically, an effective preventive maintenance plan starts with a catalog of facility equipment. This catalog includes basic information on every piece of equipment in the facility — such as location, name, serial number and vendor, as well as information on how frequently the machine should be inspected or maintained.

Keeping spotty or incomplete records can make a preventative maintenance plan both less effective and more expensive. For example, a partial service record may give an improper idea of how well-maintained certain equipment is. Missing machine information may also confuse service technicians, making it harder for them to properly inspect or maintain a machine.

Too-frequent maintenance checks can also become a problem over time. Every time a maintenance technician opens up a machine, they can potentially expose sensitive electronics to dust, humidity or facility contaminants, or risk damage to machine components.

A maintenance check also means some downtime, as it’s usually not safe or practical to inspect a running machine.

Using the wrong maintenance methods can also sometimes decrease a machine’s life span. For example, certain cleaning agents can damage door gaskets over time. This can eventually cause equipment like a freeze dryer to be unable to create a proper seal.

The equipment manufacturer and technicians can usually help a company know what kind of maintenance will work best and how often they should inspect or tune up a machine.

Going Beyond Preventive Maintenance

Preventive maintenance is the standard approach in most industries, but it’s no longer the cutting-edge of maintenance practices. New developments in the tech world, like new Industrial Internet of Things (IIoT) sensors and real-time artificial intelligence (AI) analysis, have enabled a new form of maintenance called predictive maintenance.

With predictive maintenance, a food processing plant can outfit their machines with an array of special sensors. These sensors track information like vibration, lubrication levels, temperature and even noise. A digital maintenance system will record that information, establishing baselines and data about normal operating levels.

Once the baseline is established, the predictive technology can use fluctuations or extreme variables to predict improper operation or machine failure. If some machine variable exceeds safe operating thresholds, the predictive maintenance system can alert facility supervisors — or, depending on what kind of control the system has, shut down a machine altogether.

The predictive approach can catch issues that may arise in-between checks in a preventive schedule. This can help reduce the frequency of maintenance checks — possibly preventing further machine damage and saving the business money on technician labor.

The data a predictive maintenance system collects can also help optimize equipment for maximum efficiency.

Implementing a predictive maintenance plan will require a bit of a tech investment, however.

Food Processors Can Save Money With the Right Maintenance Approach

Preventive maintenance isn’t just essential for food safety — done well, it can also be a major cost-saving measure for food processors.

Good recordkeeping, a regular maintenance schedule and new technology can all help a business decrease maintenance and equipment costs. For processors that want to invest more in their maintenance plans, a predictive approach can provide even better results.

Maria Fontanazza, Food Safety Tech
From the Editor’s Desk

Top 10 from the 2020 Food Safety Consortium Virtual Conference Series

By Maria Fontanazza
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Maria Fontanazza, Food Safety Tech

2020 has taken a lot away from us, but it has also taught us the importance of being able to quickly adapt (can you say…“pivot”?) to rapidly changing, dire circumstances. For Food Safety Tech, that meant shifting our in-person annual Food Safety Consortium to a virtual event. I really look forward to the Consortium each year, because we are a virtual company, and this is the one time of year that most of the Food Safety Tech and Innovative Publishing Company team are together. When we made the decision to move the event online, we really wanted to be considerate of our attendees, who more than likely were quickly developing webinar and Zoom fatigue. So we created a series of 14 Episodes that spanned from September until last week. I am not going to single out one episode or speaker/session in particular, because I think that all of our speakers and sponsors brought a tremendous amount of education to the food safety community. Thank you.

With that, the following are my top 10 takeaways from the 2020 Food Safety Consortium Virtual Conference Series—and this simply scratches the surface. Feel free to leave a comment on what you learned from our speakers and the discussions this fall.

  1. COVID-19 has served as the springboard for digital transformation, more of which we have seen in the past nine months than in the last several years or even decade. Tech advances are increasing efficiencies, adding the ability to be more predictive, giving more visibility and traceability in the supply chain and offering increased accessibility. These include: IoT; Advanced analytics; Artificial intelligence (FDA has been piloting AI technology); Graph technology used in supply chain visibility; blockchain; mixed reality; and remote monitoring.
  2. There are new responsibilities that come with being a part of America’s critical infrastructure and protecting essential frontline workers.
    • Companies must have a strong relationship (or work to build one) with local health departments and authorities
    • Name a COVID Czar at your company: This is a designated person, located both within a production facility as well as at the corporate location, who manages the bulk of the requirements and precautions that companies should be undertaking to address the pandemic.
  3. Every company should have an emergency risk management plan that centers around good communication.
  4. The COVID-19 pandemic is a reminder to us that the threat for viruses is always lurking beneath the surface. There is still work to be done on the food labs side regarding more rapid assays, leveling the playing field regarding conducting viral testing, and technology that enables labs to get safe, effective and consistent results.
  5. Lessons in sanitation: Investment in sanitation is critical, there are no shortcuts, and empower your sanitation employees, give them the tools they need to effectively do their jobs.
  6. The FDA’s FSMA Proposed Traceability rule is expected to be a “game changer”. It will lay the foundation for meaningful harmonization. FDA Deputy Commissioner for Food Policy and Response Frank Yiannas said the pandemic really put a spotlight on the fact that the U.S. food industry needs better tracking and tracing.
  7. Know your suppliers, know your suppliers, know your suppliers!
  8. Biofilms are ubiquitous, and the process of detecting and eliminating Listeria in your facility is a marathon with no finish line.
  9. Food Safety Culture is a profit center, not an overhead department.
  10. “If I’m not well, I can’t do well.” Making sure your needs are met personally and professionally plays an important role in being a better contributor to your company’s success.

As part of a special offering, we are making four episodes of the 2020 Food Safety Consortium Virtual Conference Series available on demand for free. Head to our Events & Webinars page to register to view the sessions on or after January 2021.

Are Traasdahl, Crisp
Retail Food Safety Forum

Is Programmatic Commerce the Next Wave in Supply Chain Tech?

By Are Traasdahl
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Are Traasdahl, Crisp

While COVID-19 exposed disconnects in the food supply chain, it also served as an overdue catalyst for rapid technology adoption. Food manufacturers, distributors and retailers were forced to grapple with consumer behaviors that—previously expected to occur over five years— changed within about five weeks. Faced with unprecedented demand, channel shifts and rapidly changing consumer purchasing behaviors, forward-looking brands and retailers have started to transform their business models to become highly responsive and agile.

A new approach called “programmatic commerce” may be the key to faster market insights and pivots. Taking cues from past attempts to digitize the supply chain from end-to-end, programmatic commerce uses artificial intelligence (AI) and machine learning (ML) to connect and unify critical business data across food manufacturers, distributors and retailers using common retail portals, BI and CRM tools as well as other data resources and platforms.

With a real-time unified view of channels and activity, programmatic commerce has the potential to create fully automated trade processes to optimize production, inventory management, logistics, promotions and more for both upstream and downstream supply chain activities.

To achieve the potential of programmatic commerce, real-time or near real-time data sources must be easily integrated, unified and displayed. This is in stark contrast to previous attempts to create end-to-end supply chain visibility, which often required custom or manual integrations, had costly and lengthy implementation requirements and necessitated custom reporting.

The programmatic approach is already gaining traction, enabling retailers to leverage AI and ML technology to optimize supply chains. But the real value is in taking it one step further—to tap into rich customer data, understand rapidly changing consumer behaviors and ultimately—to predict and personalize shopping experiences at scale.

Tracking and Adapting to Evolving Consumer Journeys

Consumers increasingly demand greater choice, control, personalization and transparency and companies must continuously create, track and manage a 360º view of customers’ shopping journeys to stay ahead of these trends. Fortunately, real-time data and analytical capabilities are available to supply the critical information they need to implement a programmatic commerce approach.

Among the shifts companies must track as a result of COVID-19 is the explosion in online grocery shopping. In November 2020, U.S. grocery delivery and pickup sales totaled $5.9 billion and a record high 83% of consumers intend to purchase groceries online again, signaling this trend continues as the pandemic lingers on.1 By 2025, online grocery sales are predicted to account for 21.5% of total grocery sales, representing more than a 60% increase over pre-pandemic estimates.2 A permanent shift toward online grocery shopping can be expected as consumers’ shopping and fulfillment experience continues to improve.

For consumers still shopping in stores, the pandemic also drove switches in primary physical store locations. In the United States, an estimated 17% of consumers shifted away from their primary store since the start of the pandemic.3 This was driven by increased work-from-home, which eliminated commuting routes and made different store locations more convenient, including ones closer to home.

Given the multitude of changes impacting consumer journeys during the pandemic, it is imperative that companies track relevant purchase drivers and considerations of each purchase occasion, while also taking into account their recent shopping experience. This creates the need for consistent, seamless and relevant experiences across both digital and physical channels that aligns all touchpoints with the consumer as part of their “total commerce experience.”

Multiple retailers are already pursuing this approach in the hope of retaining their “primary store” status across the totality of their consumers’ shopping experiences. Walmart recently launched a new store format to help achieve “seamless omni-shopping experiences” for its customers through a digitally enabled shopping environment. Customers can use the Walmart app to efficiently find what they’re looking for, discover new products, check pricing, and complete contactless checkout.4 Data tracked on these customers can eventually be used to create personalized recommendations and in-store activations and assistance based on their purchase history and in-store experience.

Conversely, the “digital store” is also being reimagined to align with consumers’ in-store experience to create a seamless shopping experience. For example, personalized meal planning service The Dinner Daily now offers the ability for its members to order recipe ingredients directly from Kroger and other Kroger-owned stores through The Dinner Daily app.5 Integrated data from multiple shopping platforms and consumer touchpoints can provide food manufacturers and retailers with shopper profiles, consumer experiences, and purchase history along with inventory status and other inputs to ultimately build personalized customer experiences and enhance shopper loyalty.

Applying Programmatic Commerce to Deliver Personalization to Consumers

Once armed with real-time data in a uniform format from sources ranging from consumer search analytics to retailer promotional pricing, a programmatic commerce approach can provide companies with predictive understanding of demand and supply to optimize decision making from raw materials through production through retail or direct-to-consumer.

Using online grocery shopping as an example, consumer personalization can be delivered through the accurate prediction and display of items relevant to each shopper based on shopping history, preferences, current cart selections, and other inputs such as real-time availability, marketing promotions and more.

Innovations are already in the market, including Halla, a data science company that developed a grocery-specific personalization algorithm that works with grocery retailer e-commerce platforms to create smart recommendations based on understanding of individual shoppers’ product usage and preferences.6 Another example is the Locai Solutions digital grocery platform, which applies AI to personalize recipe recommendations based on consumer preferences and purchase history and determines ingredients and quantities needed for easy incorporation into their shopping cart.7

The Path Ahead: Accelerating Technology Adoption in the Food Industry

AI and ML are already reducing waste across supply chains and enabling consumer personalization. However, currently only about 12% of retail decision-makers feel they are very effective at providing these experiences to customers and only 10% have access to the real-time data needed to achieve this goal.8

Modern programmatic commerce platforms (see Figure 1) can effectively bridge information gaps, improve inventory and distribution to prevent shortages or overages and help companies be data-ready to meet actual demand. Beyond this, a programmatic approach unlocks the next stage of customer satisfaction and loyalty, personalizing the experience during and after the pandemic.

Programmatic Commerce Platform visualization
Figure 1. Programmatic Commerce Platform visualization. (Courtesy of Crisp)

References

  1. Bishop, D. (2020). Tracking Online Grocery’s Growth. Brick Meets Click.
  2. Mercatus. (2020). The Evolution of the Grocery Customer.
  3.  Briedis, H., et al. (2020). Adapting to the next normal in retail: The customer experience imperative. McKinsey & Company.
  4. Whiteside, J. (2020). Reimagining Store Design to Help Customers Better Navigate the Omni-Shopping Experience. Walmart.
  5.  Corke, R. (2020). Our Online Ordering Connection for Kroger is Here. The Dinner Daily.
  6.  Halla. (2016). Halla Grocery Solutions.
  7. Locai. (2018). Locai Meal Planning.
  8. Bluecore. (2019). Align Technology, Data, And Your Organization to Deliver Customer Value.